Category: Edge AI

As generative AI models such as Large Language Models (LLMs) become integral to various industries, ensuring the privacy and security of sensitive data is critical. LLMs often interact with sensitive corporate or even personal data during training and inference, posing significant risks to privacy; such challenges must be addressed by de-identifying and securing sensitive information throughout the AI lifecycle.

Category: Power and Energy

Onshore drilling rigs operate in remote, hazardous environments where safety, reliability, and operational continuity are pivotal. The complexity of these environments requires robust OT (Operational Technology) security to protect sensitive industrial control systems (ICS) from cyber threats, unauthorized access, and operational disruptions.

Category: Edge AI

Gun-related violence presents an escalating threat to public safety, emphasizing the urgent need for faster detection and response to minimize casualties. Traditional security systems reliant on human monitoring or reactive alerts often face delays in response times, increasing the risk of harm.

Category: SD-WAN

To enhance secure, resilient, and efficient communication between remote ATMs, bank branches, and headquarters in the era of digital banking. As financial organizations increasingly adopt advanced self-service and digital solutions, deploying a robust SD-WAN architecture using uCPE (Universal Customer Premises Equipment) is essential for maintaining reliable end-to-end connectivity across distributed locations.

Category: Network Computing

In today’s AI-driven landscape, modern AI infrastructure faces several core challenges: handling massive data volumes, managing high-speed GPU-accelerated computations, and ensuring low-latency networking across AI resources. These  infrastructures must support parallel processing for demanding tasks like AI training and inferencing, necessitating an advanced, high-throughput network to deliver seamless, secure, and efficient AI workloads.

Category: Edge AI

As 5G networks evolve, they enable ultra-low latency, faster speeds, and massive device connectivity. However, traditional CPU-based systems have struggled to handle computationally intensive virtualized Radio Access Network (vRAN) functions, creating bottlenecks in performance. To address these challenges, integrating AI at the edge—where data processing is closer to the source—has become essential. AI-accelerated infrastructure optimizes latency and bandwidth usage while supporting critical network requirements such as software-programmable Network Operating Systems (NOS) for RAN operations.

Category: Edge AI

With the growing reliance on Generative AI and Large Language Models (LLMs) across industries, organizations are increasingly focusing on secure, high-performance, and cost-effective AI solutions. However, centralizing LLM training and inferencing in the cloud introduces challenges, including data privacy risks, high transmission costs, latency issues, and dependence on constant cloud communication. The demand for edge-based AI infrastructure is rising as enterprises seek to harness AI capabilities while maintaining control over sensitive data.

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